Image segmentation is an extensively studied field in computer vision. The task requires dense pixel-wise predictions, given an input image. Encoder-decoder architectures are mostly commonly used for this task. The U-Net architecture, designed by Ronneberger et al.[1]} is a very popular architecture for image segmentation. It improves over standard encoder-decoder architectures by incorporating skip connections between the encoder and decoder, allowing deep models to be trained. Standard CNN models like Resnet[2]} are often used as encoders in U-Net based architectures. These CNN models perform successive downsampling of feature maps to discard irrelevant information. This also has the effect of increasing the effective receptive field of the convolution kernels in deeper layers. While this is an effective approach for image classification, for dense pixel wise predictions, this discarding of information is harmful, especially when the object of interest is not spatially dominant.
